import torch
import torch.nn as nn

class BottleneckBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride, bottleneck_ratio=1):
        super(BottleneckBlock, self).__init__()
        self.expansion = bottleneck_ratio
        self.conv1 = nn.Conv2d(in_channels, in_channels * self.expansion, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels * self.expansion)
        self.conv2 = nn.Conv2d(in_channels * self.expansion, in_channels * self.expansion, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(in_channels * self.expansion)
        self.conv3 = nn.Conv2d(in_channels * self.expansion, out_channels, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(out_channels),
        )

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.expansion != 1 or x.shape != out.shape:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

class RegNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000, bottleneck_ratio=1):
        super(RegNet, self).__init__()
        self.in_channels = 32
        self.conv1 = nn.Conv2d(3, self.in_channels, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channels)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 128, layers[0], stride=2, bottleneck_ratio=bottleneck_ratio)
        self.layer2 = self._make_layer(block, 256, layers[1], stride=2, bottleneck_ratio=bottleneck_ratio)
        self.layer3 = self._make_layer(block, 512, layers[2], stride=2, bottleneck_ratio=bottleneck_ratio)
        self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, bottleneck_ratio=bottleneck_ratio)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(1024 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, blocks, stride, bottleneck_ratio):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, bottleneck_ratio=bottleneck_ratio))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels, stride=1, bottleneck_ratio=bottleneck_ratio))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

def regnetY_8gf(num_classes):
    return RegNet(BottleneckBlock, [2, 3, 8, 2], num_classes=num_classes, bottleneck_ratio=4)

